Analysis of Glyphosate Residues in Foods from the Canadian Retail Markets between 2015 and 2017
Bibliographic record
Abstract
Underlying the risk management of pesticides to protect human health and to facilitate trade among nations are sound scientific data on the levels of compliance with standards set by governments and internationally from monitoring of the levels of pesticides in foods. Although glyphosate is among the universally used pesticides in the world, monitoring has been hampered by the analytical difficulties in dealing with this highly polar compound. Starting in 2015, using liquid chromatography/tandem mass spectrometry (LC-MS/MS) that permits accurate and reproducible determination of glyphosate, the prevalence, concentrations, and compliance rates were determined. In this work, the glyphosate residues contents of 7955 samples of fresh fruits and vegetables, milled grain products, pulse products, and finished foods collected from April 2015 to March 2017 in the Canadian retail market are reported. A total of 3366 samples (42.3%) contained detectable glyphosate residues. The compliance rate with Canadian regulations was 99.4%. There were 46 noncompliant samples. Health Canada determined that there was no long-term health risk to Canadian consumers from exposure to the levels of glyphosate found in the samples of a variety of foods surveyed. The high level of compliance (99.4% of samples with the Canadian regulatory limits) and the lack of a health risk for noncompliant samples indicate that, with respect to glyphosates, the food available for sale in Canada is safe.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".